AI Coding Team Norms: What Developers Owe Reviewers Now
AI coding team norms need to define ownership, disclosure, validation, and reviewer rights before AI-generated PRs reach review.
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Articles tagged Software Development in the Journal.
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AI Culture5 minRead more 
AI Architecture5 minAI Coding Tools: Choose by Control Surface, Not Model Quality
Your team does not need another model ranking. It needs to decide where control lives, who owns shared state, and when MCP-connected project state becomes the missing layer.
AI Careers5 minJunior Developers and AI: Build the Discipline Agents Cannot Fake
Junior developer hiring is tighter, but AI has not erased the career path. It has moved the on-ramp toward specs, review, tests, permissions, and durable workflow state.
AI Architecture5 minSpec-Driven Development Tools: Choose by Where Project Memory Lives
Spec-driven development tools are not converging on one winner. The useful decision is where durable project memory lives after the AI session ends: in specs, in git, or in an external MCP-connected board.
AI Architecture5 minProject Board for Claude Code: When Local Memory Stops Scaling
A Claude Code project board is a shared MCP-readable system of record for scope, decisions, tasks, artifacts, and workflow locks. Here is when local memory is enough, when it stops scaling, and how Agiflow keeps state durable across sessions.
Best Practices5 minClaude Code Tasks Limitations: Where Native Tasks Stop for Teams
Claude Code Tasks is useful local orchestration, but it is not a shared system of record. This refreshed guide maps what Tasks stores, how it differs from sessions, memory, and artifacts, and when teams need an MCP-readable project board.
AI Architecture5 minWhy AI Coding Agents Lose Context: The Durable State Fix
AI coding agents lose context when the prompt becomes both working memory and system of record. The durable fix is task state: scope, criteria, tests, status, artifacts, and handoff notes.
Best Practices5 minHow To Run AI Coding Agents Across Machines Without Duplicate Work
More AI coding agents only help when ownership, state, and proof survive outside a single session. This refreshed guide shows how Agiflow uses CLI runners, workflow locks, atomic claims, device identity, and artifacts to coordinate agent work across machines without duplicate branches or lost handoffs.
Best Practices5 minHow to Structure Frontend Codebases for AI Coding Agents
AI coding agents generate better frontend code when the codebase gives them reusable components, Storybook states, design tokens, scaffolds, validation gates, and durable task context they can retrieve instead of reinventing.
AI Architecture5 minToken Efficiency in AI-Assisted Development: A Tool Architecture Guide
Token efficiency is a tool-architecture problem, not just prompt hygiene. Use Agiflow benchmark data, MCP guidance, and production measurement rules to reduce AI coding token use without hiding reliability costs.
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